Gliomas are the most common type of brain tumor in adults and are associated with poor prognosis and high mortality. Despite technological advances, their classification remains challenging in both clinical practice and research, highlighting the need for improved molecular subtyping to enhance diagnosis and treatment. In this study, we systematically evaluate the robustness of current glioma classification using a reproducible pipeline for unsupervised patient stratification. We apply multiple multi-view clustering methods, namely, CIMLR, intNMF, and moCluster, to cluster glioma patients based on transcriptomics and methylomics data. All methods consistently distinguished glioblastoma (GBM) from lower-grade gliomas, with survival analysis confirming poorer outcomes for GBM clusters, in line with clinical expectations. The best partition was obtained with CIMLR, closely aligning with the 2021 WHO classification of central nervous system tumors. In contrast, intNMF identified three clusters with distinct survival distributions, suggesting additional biological heterogeneity that warrants further investigation. To enhance robustness and better capture tumor complexity, we implemented consensus clustering to integrate results across methods. This integrative approach yielded well-defined groups with improved concordance to established glioma subtypes compared to individual methods. Notably, the consensus solution identified more clusters than the traditional classification, revealing deeper molecular characterization and providing a framework to refine glioma classification. Furthermore, we identified key molecular features supported by the literature, including both established and emerging biomarkers. Overall, our findings underscore the value of multi-omics integration and consensus clustering in refining glioma classification, supporting the discovery of novel biomarkers to improve diagnostics, prognostics, and patient outcomes.
Kappa is a graph-rewriting language originally developed for modeling molecular interactions in cellular processes. A key feature of Kappa models is that, inspired by organic chemistry, interaction rules operate on patterns i.e., partially specified molecular species, thus allowing compact descriptions of otherwise large or even infinite models. In this paper, we propose and implement a framework for statistical model checking for stochastic Kappa models against properties written in bounded linear temporal logic (BLTL). A key feature of our approach is that temporal properties operate over Kappa patterns, making the specification language native to Kappa and avoiding the expensive—and sometimes theoretically impossible—translation of the model into an equivalent chemical reaction network with a finite number of species. Concretely, given a Kappa model and a property, we first instrument the model by introducing additional variables and observables that track the truth value of each atomic proposition. For each simulated trace, the BLTL formula satisfaction is evaluated using an offline monitoring procedure. Finally, the satisfaction probability is statistically estimated from repeated model simulations. The proposed model checking framework enables a systematic exploration of behavioral properties of Kappa models, that is computationally efficient while able to capture stochastic and finite size effects with any predefined desired accuracy/precision. As such, it can serve in applications for e.g. model selection, property-driven parameter exploration, or robustness analysis. The framework is illustrated on representative case studies from systems biology and swarm robotics.
In this work, we study the source-target control problem of Boolean networks, which has important applications for cellular reprogramming. More specifically, we want to perturb the input Boolean network such that it leaves a predetermined source attractor to eventually find itself in the target attractor. We use edge perturbations, which modify the update functions of a Boolean network without necessarily setting them to constants. This work improves an existing method in which edge perturbations are used to eliminate all but the target attractor. Both approaches are based on Thomas’ first rule: the existence of at least one positive cycle in the interaction graph of a dynamical system is a necessary condition for the existence of multiple steady states. Thus, if all positive cycles are removed, the resulting dynamical system has a single steady state. This requires removing a subset of edges so that at least one edge of every cycle in the graph is perturbed: the feedback edge set. Information on the source and target attractors allows reducing the number of relevant feedback edge sets and, subsequently, candidate perturbations. We propose a carefully designed strategy that combines exhaustive search and a heuristic inspired by Simulated Annealing to effectively reduce the number of required perturbations with respect to the feedback edge set. We evaluate our method on a wide array of Boolean networks from the literature to demonstrate its efficacy and efficiency.
Aging and Alzheimer’s disease are complex multifactorial processes driven by interacting genetic and metabolic mechanisms, for which single-gene approaches often fail to capture system-level behaviour. Systems biology addresses this challenge by integrating omics data with genome-scale metabolic models (GSMMs) to identify candidate interventions. The Metabolic Transformation Algorithm (MTA) and its robust variant rMTA use constraint-based modelling to predict perturbations that shift a system from a disease-associated toward a healthier metabolic state. To move beyond single-gene prioritization, we applied EA-rMTA, an evolutionary extension of rMTA that searches the combinatorial knockout space by optimizing the robust Transformation Score. We evaluated the framework in two case studies: lifespan-extending unc-62 knockdown in Caenorhabditis elegans and early-onset Alzheimer’s disease (EOAD) in human cortex. In both settings, rMTA found biologically coherent targets and pathway-level signatures consistent with known hallmarks of aging and neurodegeneration. EA-rMTA further identified compact and interpretable multi-gene intervention strategies that outperformed single-gene deletions in shifting the metabolic state toward the desired phenotype. Together, these results show that evolutionary search extends metabolic transformation analysis beyond single-gene perturbations and enables the discovery of experimentally tractable combinatorial intervention hypotheses. More broadly, the framework provides a computational strategy for generating systems-level therapeutic hypotheses in aging, neurodegeneration, and other multifactorial disorders.
